BAyesian Model-Building Interface (Bambi) in Python#
Bambi is a high-level Bayesian model-building interface written in Python. It works with the probabilistic programming frameworks PyMC3 and is designed to make it extremely easy to fit Bayesian mixed-effects models common in biology, social sciences and other disciplines.
Bambi is tested on Python 3.6+ and depends on NumPy, Pandas, PyMC3, PyStan, Patsy and ArviZ (see requirements.txt for version information).
The latest release of Bambi can be installed using pip:
pip install bambi
Alternatively, if you want the bleeding edge version of the package, you can install from GitHub:
pip install git+https://github.com/bambinos/bambi.git
A simple fixed effects model is shown below as example.
from bambi import Model import pandas as pd # Read in a tab-delimited file containing our data data = pd.read_table('my_data.txt', sep='\t') # Initialize the model model = Model(data) # Fixed effects only model results = model.fit('DV ~ IV1 + IV2', draws=1000, chains=4) # Use ArviZ to plot the results az.plot_trace(results) # Key summary and diagnostic info on the model parameters az.summary(results) # Drop the first 100 draws (burn-in) results_bi = results.sel(draw=slice(100, None))
For a more in-depth introduction to Bambi see our Quickstart or our set of example notebooks.
We welcome contributions from interested individuals or groups! For information about contributing to Bambi, check out our instructions, policies, and guidelines here.
See the GitHub contributor page.
- Getting Started
- User Guide
- Creating a model
- Model specification
- Fitting the model
- Specifying priors
- Generalized linear mixed models
- Accessing back-end objects
- Logistic Regression
- Multi-level Regression
- Multiple Regression
- Bayesian Workflow (Police Officer’s Dilemma)
- Bayesian Workflow (Strack RRR Analysis Replication)
- Logistic Regression and Model Comparison with Bambi and Arviz
- API Reference